Modeling winter season fluxes of water and energy in a temperate montane forest

2021 ◽  
Author(s):  
Tomas Vogel ◽  
Michal Dohnal ◽  
Jana Votrubova ◽  
Jaromir Dusek ◽  
Miroslav Tesar
Keyword(s):  
Winter Season ◽  
Temporal Distribution ◽  
Montane Forest ◽  
Surface Fluxes ◽  
Mountainous Areas ◽  
Near Surface ◽  
High Resolution Data ◽  
Highly Sensitive ◽  
Headwater Catchment

<p>Winter regimes affect significantly the long-term water and energy balance of mountainous areas in Central Europe. A recently developed numerical model is used to study near-surface fluxes of water and energy in the Liz catchment — a small headwater catchment of the Otava River, situated in the Southern Bohemia. The results of the numerical simulations are compared with high-resolution data recorded at the site of interest. The forest floor of the catchment is mostly covered by snow during winter. However, the snowpack is usually exposed to several snowmelt episodes over the season. The intensity, duration and frequency of these episodes is irregular and seems to be highly sensitive to changing climate. Increasing frequency of winter periods with limited or missing snow cover affects both water flow and heat transport in the catchment. Changes in the temporal distribution of snowmelt are reflected in changing runoff patterns.</p><p> </p>

Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling

2006 ◽  
Vol 19 (13) ◽  
pp. 3088-3111 ◽  
Author(s):  
Justin Sheffield ◽  
Gopi Goteti ◽  
Eric F. Wood
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrologic Cycle ◽  
Surface Fluxes ◽  
Specific Humidity ◽  
Meteorological Variables ◽  
Coupled Models ◽  
Near Surface ◽  
Solid Precipitation

Abstract Understanding the variability of the terrestrial hydrologic cycle is central to determining the potential for extreme events and susceptibility to future change. In the absence of long-term, large-scale observations of the components of the hydrologic cycle, modeling can provide consistent fields of land surface fluxes and states. This paper describes the creation of a global, 50-yr, 3-hourly, 1.0° dataset of meteorological forcings that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature, and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation, which have been found to exhibit a spurious wavelike pattern in high-latitude wintertime. Wind-induced undercatch of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0° by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3 hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave radiation, specific humidity, surface air pressure, and wind speed) are downscaled in space while accounting for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project (GSWP2). The final product provides a long-term, globally consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.

Spatial and temporal distribution of rainfall erosivity in New Zealand

Soil Research ◽  
2015 ◽  
Vol 53 (7) ◽  
pp. 815 ◽  
Author(s):  
Andreas Klik ◽  
Kathrin Haas ◽  
Anna Dvorackova ◽  
Ian C. Fuller
Keyword(s):  
New Zealand ◽  
Temporal Distribution ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
High Resolution Data ◽  
Topographic Features ◽  
Erosion Processes ◽  
Precipitation Records ◽  
First Time

Rainfall and its kinetic energy, expressed by rainfall erosivity, drives soil erosion processes by water. One of the most commonly used erosivity parameters is the rainfall-runoff erosivity factor R of the Revised Universal Soil Loss Equation. The goal of this study was to investigate for the first time the spatial distribution of annual rainfall erosivity in New Zealand. High-resolution data from 35 weather stations were used to calculate the R-factors. Based on these results, region-specific equations were developed and were applied by using long-term precipitation records from 597 stations. The values were interpolated with a geographic information system to generate a map showing spatial variations of rainfall erosivity. Annual R-values vary across both islands by a factor of 30, from <550 MJ mm ha–1 h–1 in parts of Central Otago to >16 000 MJ mm ha–1 h–1 in the Southern Alps. These large differences are related to climatic and topographic features. Nevertheless, the data show a high correlation to the precipitation. In most parts of New Zealand, highest erosivity values occurred in December and January, whereas the lowest values were observed in August.

Ovarian Dysfunction in Fetally Irradiated Beagles

1977 ◽  
Vol 35 ◽  
pp. 658-659
Author(s):  
T. M. Seed ◽  
M. H. Sanderson ◽  
D. L. Gutzeit ◽  
T. E. Fritz ◽  
D. V. Tolle ◽  
...  
Keyword(s):  
Gamma Rays ◽  
Low Dose ◽  
Long Term Effects ◽  
Ovarian Dysfunction ◽  
Dose Rates ◽  
Highly Sensitive ◽  
Microscopic Evaluation ◽  
Ovarian Pathology ◽  
Normal Offspring

The developing mammalian fetus is thought to be highly sensitive to ionizing radiation. However, dose, dose-rate relationships are not well established, especially the long term effects of protracted, low-dose exposure. A previous report (1) has indicated that bred beagle bitches exposed to daily doses of 5 to 35 R 60Co gamma rays throughout gestation can produce viable, seemingly normal offspring. Puppies irradiated in utero are distinguishable from controls only by their smaller size, dental abnormalities, and, in adulthood, by their inability to bear young.We report here our preliminary microscopic evaluation of ovarian pathology in young pups continuously irradiated throughout gestation at daily (22 h/day) dose rates of either 0.4, 1.0, 2.5, or 5.0 R/day of gamma rays from an attenuated 60Co source. Pups from non-irradiated bitches served as controls. Experimental animals were evaluated clinically and hematologically (control + 5.0 R/day pups) at regular intervals.

scholarly journals Variation Characteristics and Transportation of Aerosol, NO2, SO2, and HCHO in Coastal Cities of Eastern China: Dalian, Qingdao, and Shanghai

2021 ◽  
Vol 13 (5) ◽  
pp. 892
Author(s):  
Xiaomei Li ◽  
Pinhua Xie ◽  
Ang Li ◽  
Jin Xu ◽  
Zhaokun Hu ◽  
...  
Keyword(s):  
Eastern China ◽  
Temporal Distribution ◽  
Correlation Coefficients ◽  
Optical Absorption Spectroscopy ◽  
Near Surface ◽  
Mixing Ratios ◽  
Variation Characteristics ◽  
Spatio Temporal ◽  
Gas Volume ◽  
Transport Flux

This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM2.5 measured by the in situ instrument and the PM2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO2 and aerosols, but not for SO2 and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO2 in Shanghai and HCHO in Qingdao. PM2.5 presented the largest transport flux, followed by NO2 and SO2. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO2 and SO2 by CAMS is due to its overestimation of near-surface gas volume mixing ratios.

scholarly journals Biodiversity of butterflies in endosulfan-affected areas of Kerala, India

2020 ◽  
Vol 81 (1) ◽  
Author(s):  
K. N. Raghavendra ◽  
Kumar Arvind ◽  
G. K. Anushree ◽  
Tony Grace
Keyword(s):  
Monsoon Season ◽  
Winter Season ◽  
Wiener Index ◽  
Long Term Effects ◽  
Affected Area ◽  
Butterfly Assemblages ◽  
Abundance And Diversity ◽  
The One ◽  
One Way Anova

Abstract Background Butterflies are considered as bio-indicators of a healthy and diversified ecosystem. Endosulfan was sprayed indiscriminately in large plantations of Kasaragod district, Kerala which had caused serious threats to the ecosystem. In this study, we surveyed the butterflies for their abundance and diversity in three differentially endosulfan-affected areas viz., Enmakaje—highly affected area, Periye—moderately affected area, Padanakkad—unaffected area, carried out between the end of the monsoon season and the start of the winter season, lasting approximately 100 days. Seven variables viz., butterfly abundance (N), species richness (S), Simpson’s reciprocal index (D), the Shannon–Wiener index (H′), the exponential of the Shannon–Wiener index (expH′), Pielou’s evenness (J) and species evenness (D/S), related to species diversity were estimated, followed by the one-way ANOVA (F = 25.01, p < 0.001) and the Kruskal-Wallis test (H = 22.59, p < 0.001). Results A population of three different butterfly assemblages comprised of 2300 butterflies which represented 61 species were encountered. Our results showed that Enmakaje displayed significantly lower butterfly diversity and abundance, compared to the other two communities. Conclusion So far, this is the first study concerning the effect of endosulfan on the biodiversity of butterfly in the affected areas of Kasaragod, Kerala, India. This study may present an indirect assessment of the persisting effects of endosulfan in the affected areas, suggesting its long-term effects on the ecosystem.

Long-term performance of zeolite Na A-X blend as backfill material in near surface disposal vault

2009 ◽  
Vol 149 (1-3) ◽  
pp. 143-152 ◽  
Author(s):  
R.O. Abdel Rahman ◽  
H.A. Ibrahim ◽  
N.M. Abdel Monem
Keyword(s):  
Near Surface ◽  
Backfill Material ◽  
Long Term Performance ◽  
Term Performance

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

scholarly journals On the Use of Rotary-Wing Aircraft to Sample Near-Surface Thermodynamic Fields: Results from Recent Field Campaigns

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 10 ◽  
Author(s):  
Temple Lee ◽  
Michael Buban ◽  
Edward Dumas ◽  
C. Baker
Keyword(s):  
Weather Prediction ◽  
Chemical Properties ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Spatiotemporal Variability ◽  
Near Surface ◽  
Field Campaigns ◽  
High Level ◽  
Rotary Wing

Rotary-wing small unmanned aircraft systems (sUAS) are increasingly being used for sampling thermodynamic and chemical properties of the Earth’s atmospheric boundary layer (ABL) because of their ability to measure at high spatial and temporal resolutions. Therefore, they have the potential to be used for long-term quasi-continuous monitoring of the ABL, which is critical for improving ABL parameterizations and improving numerical weather prediction (NWP) models through data assimilation. Before rotary-wing aircraft can be used for these purposes, however, their performance and the sensors used therein must be adequately characterized. In the present study, we describe recent calibration and validation procedures for thermodynamic sensors used on two rotary-wing aircraft: A DJI S-1000 and MD4-1000. These evaluations indicated a high level of confidence in the on-board measurements. We then used these measurements to characterize the spatiotemporal variability of near-surface (up to 300-m AGL) temperature and moisture fields as a component of two recent field campaigns: The Verification of the Origins of Rotation in Tornadoes Experiment in the Southeast U.S. (VORTEX-SE) in Alabama, and the Land Atmosphere Feedback Experiment (LAFE) in northern Oklahoma.

Sign in / Sign up

Export Citation Format

Share Document